Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland.
J Chem Theory Comput. 2020 Apr 14;16(4):2436-2449. doi: 10.1021/acs.jctc.9b01207. Epub 2020 Mar 24.
Mastering artificial water oxidation is a key step on moving away from fossil fuels toward a carbon emission-free society. Unfortunately, the crucial chemical transformation of this reaction, the O-O bond formation, is still not well understood, even though there are various known active water oxidation catalysts, such as Ru-based catalysts bearing a Py5 ligand. Those were recently investigated both experimentally and using a static density functional theory (DFT) approach based on geometry optimizations. In this work, we shed light on the O-O formation catalyzed by those Ru-based complexes, utilizing enhanced sampling techniques such as the Bluemoon ensemble and metadynamics together with high-performance DFT-based molecular dynamics simulations. This allowed unprecedented detailed insights into the process of the oxygen-oxygen bond formation and also extended the view on the reaction network and the flexibility of the product state because of the consideration of the dynamics at ambient conditions. Our model system contained both the catalyst and a large number of explicit water molecules which can participate in the reaction and stabilize intermediates. Moreover, it is demonstrated how crucial the choice of the collective variable is in order to capture relevant features of the studied reaction.
掌握人工水氧化是摆脱化石燃料向碳排放为零的社会迈进的关键步骤。不幸的是,尽管有各种已知的活性水氧化催化剂,如带有 Py5 配体的 Ru 基催化剂,但该反应的关键化学转化,即 O-O 键形成,仍然没有得到很好的理解。最近,人们通过实验和基于几何优化的静态密度泛函理论 (DFT) 方法对其进行了研究。在这项工作中,我们利用增强采样技术(如 Bluemoon 集合和元动力学)以及基于高性能 DFT 的分子动力学模拟,研究了这些 Ru 基配合物催化的 O-O 形成。这使得我们能够以前所未有的详细程度深入了解氧氧键形成的过程,并且由于考虑了环境条件下的动力学,我们对反应网络和产物状态的灵活性有了更深入的了解。我们的模型系统包含催化剂和大量可以参与反应并稳定中间体的显式水分子。此外,还证明了选择合适的集体变量对于捕捉所研究反应的相关特征是多么重要。